KAUST Grant NumberKUK-C1-013-04
Online Publication Date2011-02-15
Print Publication Date2012-02-10
Permanent link to this recordhttp://hdl.handle.net/10754/600163
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AbstractThe variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis. In either of these scenarios, it can be important to assess uncertainties in the assimilated state. Ideally, it is desirable to have complete information concerning the Bayesian posterior distribution for unknown state given data. We show that complete computational probing of this posterior distribution is now within the reach in the offline situation. We introduce a Markov chain-Monte Carlo (MCMC) method which enables us to directly sample from the Bayesian posterior distribution on the unknown functions of interest given observations. Since we are aware that these methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk-type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however, more sophisticated MCMC methods are available which exploit derivative information. For simplicity of exposition, we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number flow in a two-dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces. © 2011 John Wiley & Sons, Ltd.
CitationCotter SL, Dashti M, Stuart AM (2011) Variational data assimilation using targetted random walks. Int J Numer Meth Fluids 68: 403–421. Available: http://dx.doi.org/10.1002/fld.2510.
SponsorsA. M. S. is grateful to EPSRC, ERC and ONR for financial support. M. D. was supported by the Warwick Postgraduate Fellowship fund and, as a postdoc, by the ERC. The research of S. L. C. has received a postgraduate funding from EPSRC, and a postdoctoral funding from the ERC (under the European Community's Seventh Framework Programme (FP7/2007-2013)/ ERC grant agreement No. 239870) and from Award No KUK-C1-013-04, made by the King Abdullah University of Science and Technology (KAUST).